Jeremy Robertson | Machine Learninghttp://jeremyrobertsonmachinelearning.com
Jeremy Robertson, founder of Lockwood Executive Search recruiting firm, takes interest in technology and machine learning. Mon, 15 Oct 2018 20:01:56 +0000en-UShourly1https://wordpress.org/?v=4.5.2How Might ML Change Investing in 2019?http://jeremyrobertsonmachinelearning.com/how-might-ml-change-investing-in-2019/
Mon, 15 Oct 2018 20:01:56 +0000http://jeremyrobertsonmachinelearning.com/?p=183Machine Learning (ML) and Artificial Intelligence (AI) continue to comprise some of the hottest stocks for investors in 2018 — with names like Amazon and NVIDIA capitalizing on the trend thanks to their respective forays into cognitive technology — but they have also remained harbingers for major change within the investing industry as a whole. Today, there are more instances of ML in finance than ever before, with ML tools and computing capabilities increasing in both power and frequency concurrently; this has spawned from cognitive trends surrounding fraud detection, algorithmic trading, loan underwriting, and a variety of other significant industry dimensions.

As this technology has become an integral part of the financial world, it comes as little surprise that more key trends rest on its immediate horizon. That said, here are a few notable considerations in ML-based investing as we near the start of 2019.

Sentiment analysis

Perhaps the most alarming ML implications surround the concept of sentiment analysis, or the technology’s ability to understand and analyze news stories, social media interactions, and other data sources at a human level, transcending the mere absorption of stock prices and trades alone. As observed by TechEmergence, “the stock market moves in response to a myriad human-related factors that have nothing to do with ticker symbols (like those commonly showcased in the aforementioned news stories and data sources),” and ML algorithms are being designed to replicate such “human intuition” at an enhanced level by reading and accurately interpreting potential trends.

Automated advisement

ML algorithms are becoming capable of recommending financial services and products — based on any number of individualized metrics — and this process only continues to become more personalized as the technology evolves. In the coming year, expect these types of automated services to be perceived as “more trustworthy, objective, and reliable –” maybe even more so than human advisors. This notion is exciting, especially when considering its impact on portfolio advisement, insurance recommendation, and overall financial planning (among other areas); quick and convenient automation of these services are already changing the financial landscape, and increased efficiency will serve as a natural complement to current activity.

Security

It is no secret that ML has led to increased security in both investing and a wide variety of other industries. Cognitive technologies are already able to accurately detect fraud despite a growing “perfect storm” of data-based security risks, detecting unusual activities and flagging them as potentially dangerous anomalies. The key now is to make sure these services are as accurate and safe as possible, as the call for security enhancement has only grown louder in the age of investing apps rooted in e-commerce and online banking.

Looking ahead, investing-based ML security will strive to sharpen itself by mitigating flagged “false positives,” or nonthreatening activity that is flagged as a risk, while also challenging longstanding paradigms in cybersecurity’s storied history; this means the potential elimination or reimagining of passwords, usernames, and similar protective forces to keep users as secure as possible.

]]>Proven Strategies in Algorithmic Trading (Pt. 1)http://jeremyrobertsonmachinelearning.com/proven-strategies-in-algorithmic-trading-pt-1/
Mon, 15 Oct 2018 18:16:23 +0000http://jeremyrobertsonmachinelearning.com/?p=179As I touched on in a previous blog, algorithmic trading provides countless benefits that complement and enhance those of traditional trading approaches — the main ones being an increase in speed, a general consolidation of time otherwise spent managing numerous trade aspects, and a removal of potential folly rooted in human emotion. As a result of these perks, the technology has become prominent in many different financial markets.

There are a variety of effective strategies contained in algorithmic trading, whether you are starting from scratch or striving to improve upon past forays into the algorithmic cycle. Here now are several of those strategies at a glance.

Moving Average Crossover Strategy

If you are experienced in trading, you likely already know quite a bit about Simple Moving Average (SMA); it is the most basic of moving averages used in trading. SMA is calculated via any predfined or fixed number of days, and when implemented in a quantitative trading strategy, it can be broken down into a simple algorithmic formula sometimes referred to as Moving Average Crossover Strategy:

Calculate SMA of five days

Calculate SMA of 20 days

Finally, “take a long position when the five days SMA is larger than or equal to the 20 days SMA” (and a short position if it is smaller).

If, however, you are new to algorithmic trading, it is important to recognize that the aforementioned is a relatively simple strategic example. As you continue to delve into the process, it will become increasingly evident that more complex algorithms stand as the norm within trading strategy. Still, it is never illogical to start small as you learn the language.

Trend Following Strategy

Trends are key in the trading landscape — the process of monitoring which directions a market is moving in and why. A trend following algorithmic strategy can help traders “produce, buy, and sell signals” following the emergence of new trends; this is a tried-and-true technique amongst seasoned traders, but one that, in this case, is put on the shoulders of trading automatons. The resulting strategy is an effective blend of traditional and modern trading ideologies, one that is both fluid and comparatively easy to implement. Technical analysis and market patterns are automatically utilized to make key trade decisions both quickly and efficiently.

Carry Trade Strategy

An asset to new and experienced traders alike, carry trade strategy is one of many strategies that has been considerably fine-tuned when paired with automation; it traditionally focuses primarily on “the difference in yield between two currencies.” Many carry trade systems have implemented algorithmic functionality — mainly to mitigate the strategy’s consistent drawbacks — namely increased volatility and susceptibility to interest rate shocks — while supplementing its benefits. Algorithms can select ideal currencies based on crucial factors like low volatility and potential profitability.

]]>Adapt and Manage: How Everchanging ML algorithms are Reshaping Investment Managementhttp://jeremyrobertsonmachinelearning.com/adapt-and-manage-how-everchanging-ml-algorithms-are-reshaping-investment-management/
Mon, 15 Oct 2018 18:05:06 +0000http://jeremyrobertsonmachinelearning.com/?p=176Machine Learning (ML) has become a broad technological facet encompassing countless industries, corporate interactions, and symbiotic technologies growing in tandem. ML concepts like deep learning and natural language processing (NLP) have revolutionized both the ways we learn from collected data and the ways we apply these findings to major investment decisions.

Investing management is no different; its ML application runs far and deep, covering everything from trend discovery and strategic support to the administration of new ideas. As 2019 rapidly closes in, ML-based investment management is expected to continue its recent trend of industry disruption and innovation. For now, though, the technology has already created a variety of exciting implications surrounding its potential.

Supplementing strategy

ML has continues to transform the construction, interpretation, and administration of investment strategies overseen by all types of managers; this was perhaps its earliest identified benefit within the industry, and it is one that has grown immensely in recent years — to the point where the industry’s most fundamental, non-quantitative managers are using ML-generated data to formulate new ideas. Both asset management and digital asset management remain “ripe for automation through AI” thanks to their existing application of voluminous data, and ML has supplemented systematic strategies for both via market movement prediction and trade execution.

Trend discovery and analysis

When left to their own devices, many ML algorithms are becoming capable of discovering and analyzing key investing trends with which to forecast future prices; they essentially gravitate towards a trend-following pattern based on compiled historical data. This capability is incredibly significant, as most quantitative research is rooted in the “discovery of linear relationships between input data (such as historical price movements, interest rates, or company earnings) and future movements in asset prices,” and this notion continues to play a pivotal role in trend discovery. As algorithms advance to transcend what has already been established by traditional trend following, they have provided additional means of dissecting trend data. That is, they are becoming capable of identifying directional market behavior stemming from trends — and the specific path taken to reach a certain price pattern. These developments stand as natural complements to existing analytical models and forecasting methods.

The place of industry relationships

The previous sections paint a promising and ambitious future for ML-based investment management, and in order for this reality to continue its ascent, industry professionals must remain harmonious both amongst themselves and with necessary academic parties set on advancing the technology. Man Group, for instance, has forged key research relationships with the University of Oxford’s Engineering Science Department to supplement the efforts of its various technicians, scientists, and investing specialists. This is the type of comprehensive working mosaic that will allow ML management to reach new heights as investing’s potential new norm.

That said, North America remains one of the most prominent regions for market expansion, contributing high amounts of revenue “due to technological developments and considerable application of algorithm trading;” this comes as little surprise, as an increasing amount of major industry players have shifted to an automated interface — a notion recently exhibited by Goldman Sachs, which now predicates much of its trading activity on “complex trading algorithms, some with machine learning (ML) capabilities.”

Moving forward, algorithmic trading looks to jump from a promising trend to a full-fledged paradigm shift across investing’s vast majority.

Revisiting the drawing points

At its core, the allure of algorithmic trading is as simple as that of any ML-based concept: automation is quicker and easier; it can streamline complex processes, therefore generating more turnover without sacrificing efficiency. However, the benefits run much deeper than a mere surge in industry convenience, and this fact will play a big part in the market’s increased adoption amongst reluctant companies.

Algorithmic trading is able to execute trading commands — some significantly layered — with accuracy while mitigating both latency and potential setbacks rooted in human folly. Now, this process is increasing in speed, “happening in the span of microseconds and going on to nanoseconds. Pair this notion with customization, cost-effectiveness, and anonymity, and you are left with widespread market appeal rooted in a variety of industry facets.

Regulations and growing capabilities

When looking at potential regulations for algorithmic trading, one must consider both the elimination of threats and the pace at which innovation is allowed to occur — that is, safety must be emphasized in both a firm but flexible manner. Regulators will need to be well-versed in algorithmic operations while remaining available to embrace and adapt to changing legislations — especially as the technology’s full capabilities become further recognized. Identified challenges, in this regard, currently include “insufficient risk valuation capabilities and operational efficiencies” within an already fragmented market.

As for what these future capabilities may look like, future algorithmic systems may reach the point of harnessing archived historical data from trading’s entire history, allowing us to better determine which approaches worked, which ones did not, and which ones might work in the future. Additionally, as QuantInsti observes, these systems have grown closer to a variety of other exciting possibilities, including, but not limited to:

Simultaneous checking of multiple markets worldwide, saving a significant amount of time in the process.

Enhanced communication via algorithmic chips, creating the possibility of global regulation while opening the door for concepts like kill switches.

For now, the market remains healthily rooted in both traditional and algorithmic methodologies, but it is fair to assume that, as the benefits of algorithmic trading become increasingly realized, it will soon become more difficult to deny change in good faith.

]]>A closer look at computer vision in investinghttp://jeremyrobertsonmachinelearning.com/a-closer-look-at-computer-vision-in-investing/
Fri, 30 Mar 2018 17:31:21 +0000http://jeremyrobertsonmachinelearning.com/?p=169In a recent blog exploring the immediate implications of artificial intelligence (AI) and machine learning (ML) in asset management, I touched on how computer vision has risen as a significant

cognitive technological resource within the industry. This technology strives to emulate the human visual system, producing efficient recognition and high-level understanding of videos, images, and other visual mediums in an attempt to compile data and symbolic information. This data, in turn, can be referenced for a variety of reasons to “understand complex signals from visual content, including economic trends to emotion, intent, feelings, and desires.”

The finance industry — namely its investing branch — stands as one of several markets empowered or otherwise impacted by the rise of computer vision and AI. Computers are now able to absorb and analyze visual content faster and more accurately than ever before, and with a growing list of uses for this compilation of real-time data, implications for this technology in the financial sector are incredibly vast.

But what is computer vision, and how are its specific offerings enduringly relevant to modern investing? Here is a quick look at this intriguing branch of automated technology.

Finding trends

Thanks to advancements in computer vision technology, financial investors are now able to better identify economic market trends in real time. Fascinatingly, much of this information has been gathered via the analysis of satellite images. This content technically been available for years, but it has only just come into its own as a fruitful data mine thanks to concurrent growth in computer vision sophistication and data availability. As a result, investors have the ability to keep simultaneous tabs on key economic variables ranging from shipping ports to agricultural yields, building a stronger all-around knowledge of the global economy at large. By placing these responsibilities on the automated shoulders of computer vision, investors are able to stay connected to these vital trends while remaining free to focus on other key parts of their respective companies.

A bright future

Computer vision has already grown to be recognized as a cornerstone of modern AI, and as industry automatons continuously refine their ability to extract and efficiently dissect visual data, it is assumed that the financial world will collectively lean on it more to model environments based on geospatial economic data.

While other industries have been temporarily disrupted this technology’s rise to ubiquity, the finance industry appears poised to embrace it as an enabling force. Outside of key trend identification via image analysis, computer vision will smooth over lingering hindrances in user experience, quicken internal efficiencies across the board, and ultimately redefine investing workforces for the greater good of companies worldwide. There is still much to learn about computer vision’s potential, but at this point, its only restrictions appear to lie in the human imagination.

]]>AI in hedge funds: analyzing proposed problems (Pt. 1)http://jeremyrobertsonmachinelearning.com/ai-in-hedge-funds-analyzing-proposed-problems-pt-1/
Fri, 30 Mar 2018 17:27:37 +0000http://jeremyrobertsonmachinelearning.com/?p=166By this point, the exponential growth of artificial intelligence (AI) within investing has coerced many into an unstoppable — albeit potentially slow — adaptation process. Autonomous technologies have established new framework in terms of industry potential, and they could very well revolutionize hedge funding and asset managing as we know it.

However, these exciting projections have come with their share of cautionary inverses. Resistors of AI still cite a series of potential problems that have kept them hesitant — especially within quantitative hedge funds, where high risk scenarios are already a common part of the process. Here, now, is a closer look at a few of these proposed issues.

Unneeded (and ineffective) advancement?

According to the technology review, there is a chance “AI could make markets more volatile right when they lease need it.” This claim is reinforced by reports suggesting that, as a result of a lack of necessary data, the technology may lack the ability to make key presumptions about financial stability, and it could possibly act unfavorably in the event of another major recession or similar crisis. If these suggestions prove to be true, AI’s increased presence might amount to an unnecessary novelty doing little to reinforce firms for the future.

Many lingering AI fears may very well stem from these analyses, and it is easy to see why; after all, financial representation via pseudo-sentient software is as scary as it is profound. However, it is likely too early to make broad claims of AI’s perceived detriments on the industry. The impact of machine learning and deep learning have become incredibly far-reaching, proving to be useful in numerous investing facets.

Trade challenges

An increasing amount of hedge funds, new and established, have gained confidence in the autonomous handling of important trade decisions, shaking up old paradigms by trusting bots with their customers’ money. This approach has come with obstacles, but these issues have challenged AI proponents to think innovatively about finance and trade. The most frequent debate, in this regard, boils down to a classic volley of autonomous error vs. human folly — essentially, is human oversight still the dominate means of mitigating problems?

Expanding on this concept, some critics of AI suggest the technology will be unable to handle “mysterious parameters” influencing financial markets, such as politics, news events, and unexpected incidents like natural disasters. Nevertheless, when tracing the unprecedented growth of AI and ML potential in just the last few years alone, it is also logical to assume AI algorithms will one day grow smart enough to identify, absorb, and contextualize such variables in their handling of trade data.

]]>AI/ML in asset management: trends for 2018 and beyondhttp://jeremyrobertsonmachinelearning.com/aiml-in-asset-management-trends-for-2018-and-beyond/
Fri, 30 Mar 2018 17:22:58 +0000http://jeremyrobertsonmachinelearning.com/?p=162In recent years, artificial intelligence (AI) and machine learning (ML) have been projected by some to eventually assume almost 99 percent of the investing industry. Now, in light of this technology’s continued ascent towards complete sentient functionality, there remains one vital question: “how far will AI go?”

With 2018’s first quarter nearly behind us, it is safe to assume that investing-based AI/ML will, in fact, go very far, confirming the aforementioned projections in the process. An overarching corporate shift to the cyber sector has resulted in subsequent emphasis on automated asset management tactics, and now, AI/ML is slowly becoming an accepted norm across firms worldwide.

Industry implications for AI/ML remain both promising and problematic as firms work to hone their automated approach, mitigate risk, and align key drivers into a progressive extension of what they already know and practice. With these notions in mind, here are a few projections for AI/ML on asset management’s immediate horizon.

Driving change

Accenture recently identified AI/ML technology as one of the “Big Three” of asset management in 2018 (alongside the cloud and the industry’s current operating model). This observation is consistent across many commentators within the industry, and this is not surprising considering an acceleration in firms’ efforts to examine their current workforce strategies. Accenture specifically predicts that firms will “add a range of integrated automated technologies to their resource mix” this year, focusing not only on AI, but on robotic process automation (RPA) to concurrently streamline distribution strategizing, employee engagement, and risk and cost reduction — among other vital areas.

These projections are fair, as cognitive technologies have allowed firms to jumpstart their business models in exciting new ways.

Achieving change: the tools

To actually bring these changes to fruition, many funds are employing a variety of tools and strategies to push the threshold of technological possibility. These resources include, but are not limited to Deep Learning networks, which can help to boost profits, navigate complex markets, and cut costs via artificial neural networks; Natural Language Processing (NLP), a branch of ML specifically focused on human-to-machine interactions (e.g., digital assistant software); and Computer Vision, an especially prominent concept, in recent years, dedicated to the “high-level understanding of digital images or videos” with intentions of reaching parity with the human visual system. These three concepts in particular have been implemented to further bridge the gap between human cognition and technological automation — all for the sake of making asset management both trustworthy and convenient.

What will change look like?

An enduring anxiety surrounding the shift to AI/ML is a perceived threat to employment. As the product of longstanding conditioning from science fiction, unfounded mythos, and fear of the unknown, portions of the human race continue to exhibit unease towards the concept of an automaton taking full control of a human-occupied position. The reality is that, despite a growing pool of research suggesting their expansive implementation in the near future, ambitious AI/ML initiatives remain a big bet for firms to make at this point in time, and for now, their place in the industry mosaic remains a work in progress — albeit it a very promising one.

However, many firms expect to soon make these technologies ubiquitous conductors of key repetitive tasks (client onboarding, customer data input and analysis, FAQ-related response, etc.), allowing asset managers to allocate their focus in constructive new ways (for example, on the strengthening of competitive advantage).

]]>Will ML security become easier to crack in 2018?http://jeremyrobertsonmachinelearning.com/will-ml-security-become-easier-to-crack-in-2018/
Sat, 17 Feb 2018 14:37:37 +0000http://jeremyrobertsonmachinelearning.com/?p=158The past few years have collectively seen the rapid evolution of machine learning (ML) technology. This sophisticated asset has opened new doors for technology-based companies, presented fresh options on pre-existing concepts, and ultimately changed the face of what is possible in terms of autonomous human perception — and this notion is especially prevalent within modern cybersecurity. Constantly learning technology has eliminated the need for certain human involvement in the security process, all while supplementing the process as a data-driven deterrent.

However, as we move into another year of potential growth in this vital technological sector, some commentators have predicted a wave of equally sophisticated threats from the cybercrime community. Just like the gatekeeping automatons they hope to outsmart, these individuals are finding ways to learn and adapt, changing their approach in order to thrive within an ever-changing landscape.

Advancing tactics

Though unfortunate, it is undeniable that hackers and cyber terrorists have become increasingly clever in their recent operations. Now, these criminals are finding ways to exploit the very characteristic setting machine learning at the forefront of technological possibility: self-reliant activity. By infiltrating this aspect of ML-ready units, attackers could, in turn, enable digital security systems to corrupt themselves, opening windows of opportunity to harvest valuable data. To make matters worse, these situations could potentially allow criminals to achieve their goals much quicker than previously observed.

Furthermore, many criminals are developing nefarious new ways to potentially vehicalize ML technology — namely AI-enabled bots originally designed to streamline various processes. For instance, chatbots, which have already been developed to oversee financial transactions and various concierge-based services, are predicted to be a major cybercrime target in this regard; they could theoretically be used to not only crash utilities and hack protected domains, but also to potentially influence human interaction through the use of false prompts.

What can be done?

While this outlook is grim at a glance, the good news is there are ways to avoid these threats. There is arguably one broad lesson encapsulating the future of ML-based cybersecurity: certain pre-existing paradigms must shift to answer a continually evolving wave of criminal activity. This notion has already been suggested as we begin to look at longstanding aspects of the cybersecurity landscape, such as password-protected domains. While alternatives to such protections are still the topic of their own widespread debate, the reality is that passwords (and similar authentication methods) are proving to be insufficient as a combative tool towards advanced hacking attempts. Perhaps this conversation is a microcosm of the entire scenario as we currently know it; these are the topics must continue to discuss and question — our own process of changing, adapting, and, most importantly, learning from past follies.

Cybersecurity is an important variable in the professional world. The industry has seen a hiring upswing in recent years, and the need for new sharp minds only continues to increase as the cyber landscape changes.

Jeremy Robertson, founder of Lockwood Executive Search, is an experienced professional in the recruiting industry. For more information, click here.

]]>Introducing AI/ML in your workplacehttp://jeremyrobertsonmachinelearning.com/introducing-aiml-in-your-workplace/
Sat, 17 Feb 2018 14:31:27 +0000http://jeremyrobertsonmachinelearning.com/?p=155With advanced Artificial Intelligence (AI) and Machine Learning (ML) growing more prevalent across a variety of industries, you may be wondering how your business can carve its niche within this new and exciting technological trend. The good news is that, as AI and ML become increasingly sophisticated, their range of potential uses has become increasingly vast — and it has come to include many corporate settings, regardless of their provided services or products.

Here are a few ways you might be able to implement AI and ML into your workplace.

Improving meetings

An immediately useful AI/ML application is the improvement of meeting ergonomics — especially those surrounding video meetings and conference calls, two incredibly common methods of interoffice collaboration. These technologies may be used to limit the amount of noise interference between two speakers during a call, improving overall audio quality by learning to eliminate certain unwanted noises like keyboard typing, pets, and distant sirens. Furthermore, AI and ML have been helped to strengthen video aspects of a meeting, constantly reframing camera views to give everyone a clear, consistent view while keeping all participants aware of who is present and who is currently speaking.

Bot contributions

There is an entire subset of possibilities surrounding the use of AI- and ML-based chatbots. These autonomous units have already been implemented in a variety of industries, previously holding exclusive prominence within consumer culture. However, bots have only just recently become equally prevalent in the workplace, with HR teams nationwide investing in them as virtual assistants to daily operations; their capabilities may include meeting scheduling, document generation, and the tapping of various employee health- and benefits-related data. Human-based HR operations continue to be the best approach in terms of general control, but why not consider digitally supplementing parts of the process?

Thinking outside the box

Perhaps the best part about corporate-based AI and ML is its ability to inspire new, innovative approaches to longstanding office processes. Subsequent technologies stemming from autonomous learning open a whole new series of doors for employee engagement tactics, collaborative sessions, and presentations. Virtual reality (VR), for instance, continues to be explored in scenarios where synthetic projections could lead to increased foresight and improved understanding — potentially without the need to spend money on physical means of accomplishing both. For instance, Zues Kerravala of CIO discusses how VR could be integrated into team meetings centered on the dissection of an object (in his example, a heart pump) without the need to actually purchase the object for demonstrative purposes. This scenario could be beneficial both financially and educationally, and Kerravala’s example is just one of countless ways an AI-based tool could change that company’s entire approach to trainings and goals.

]]>The growing intelligence of machine learning appshttp://jeremyrobertsonmachinelearning.com/the-growing-intelligence-of-machine-learning-apps/
Sat, 17 Feb 2018 14:28:33 +0000http://jeremyrobertsonmachinelearning.com/?p=152Machine learning continues to grow as a new frontier in the modern technological landscape, with its impact felt in areas ranging form streamlined customer analytics to advanced auditory technology. At the same time, our overall digital activity has become increasingly facilitated by mobile applications found on smartphones and wearable devices.

Naturally, these two prevalent variables have become married in recent years, producing apps capable of tracking and utilizing user data — namely in-app tendencies and preferences — to create an experience that is personalized and unique.

Now, machine learning apps stand as their own hybridized benchmark in technological achievement.

Constant learning

A significant, and perhaps the most fascinating, detail of machine learning apps is their ability to learn at a nonstop rate, so long as they are used regularly. Many of these apps have grown capable of learning from the seemingly minuscule details of their users’ day-to-day lives. An easy example is the advancement of recommendation services within social media; a Facebook user, for instance, now has a vast pool of personalized options at his or her finger tips, from user suggestion based on location and mutual workplace to photo tagging via facial recognition. Other apps, especially entertainment-based tools like Netflix and Spotify, have become smart enough to not only compile suggested content based on interest and thematic preference, but they can also subdivide these preferences based on different genres, artists, and moods. In this sense, these apps respond to our feelings and passions at an almost sentient level.

Expanding boundaries in the workplace

Compared to other relevant facets of Artificial Intelligence (AI) technology, machine learning apps have gained traction as a leading recipient of funding. This notion alone is indicative of the working world’s increasing trust in this expansive technology.

As the boundaries continue to expand in terms of machine learning potential, they have grown to include corporate ventures in data mining, robotics, and finance algorithms — and now, app technology can be included as a crucial highlight in this list of uses. With many workplaces moving to a hybrid brick-and-mortar/digital layout, the ability to remain uniformly rooted in automated analytics has become vital. Subsequently, many businesses now rely on machine learning app intelligence to develop a stronger notion of customer feedback. Services like Yelp, for instance, have become a significant consideration for essentially any business capable of being reviewed, as widespread ratings can quickly make or break revenue figures based on digital reputation alone. Analytics compiled via machine learning help facilitate this process for all involved, keeping a potentially overwhelming chunk of data organized and digestible. The ability to take these offerings on the go has only increased convenience.

As these factors are already noteworthy in early 2018, they will surely continue to gain momentum throughout the year and beyond — especially in the mobile sector.